Wednesday, November 29, 2017

Alison DeNisco Rayome, Staff Writer for TechRepublic summarizes, "Machine learning offers a powerful computing tool, but most companies are not taking advantage of it, according to Deloitte."

Photo: iStockphoto/agsandrew

While machine learning offers advantages for nearly every industry,
very few companies have actually adopted this artificial intelligence
(AI) technology, and face several common barriers to entry, according to
a new Deloitte report.

Less than 10% of executives said that
their companies were investing in machine learning, according to a
recent SAP survey, and many cite barriers to adoption including
qualified staff, still-evolving tools and frameworks, and a lack of
large datasets required to train algorithms. Many people also face the
"black box" problem, in that they understand that machine learning
models generate valuable information, but are reluctant to deploy them
in production, because their inner workings are not immediately clear.

To lower the barriers to entry, Deloitte researchers identified five
"vectors of progress" that make it easier, faster, and less expensive to
deploy machine learning in the enterprise:

1. Automate data scienceDeveloping machine learning solutions requires data science skills, a field in which practitioners are in large demand and short supply.
However, as much as 80% of the work of data scientists can be fully or
partially automated, according to Deloitte, including data wrangling,
exploratory data analysis, feature engineering and selection, and
algorithm selection and evaluation.

"Automating these tasks can make data scientists not only more
productive but more effective," the report stated. A growing number of
tools from both established companies and startups can help reduce the
time required to execute a machine learning proof of concept from months
to days, Deloitte noted. This also means augmenting data scientists'
productivity, so that even with a talent shortage, enterprises can still
expand their machine learning adoption.

2. Reduce the need for training data Training
a machine learning model can require up to millions of data elements,
and acquiring and labeling this data can be time consuming and costly
for enterprises.

However, we've seen a number of techniques
emerging for reducing the amount of training data required for machine
learning. Some use synthetic data, generated with algorithms to mimic
the characteristics of the real data, and have seen strong results: A
Deloitte LLP team tested a tool that allowed it to build an accurate
model with only a fifth of the training data previously required by
synthesizing the remaining 80%. Read more... Source: TechRepublic

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Hello, my name is Helge Scherlund and I am the Education Editor and Online Educator of this personal weblog and the founder of eLearning • Computer-Mediated Communication Center.
I have an education in the teaching adults and adult learning from Roskilde University, with Computer-Mediated Communication (CMC) and Human Resource Development (HRD) as specially studied subjects. I am the author of several articles and publications about the use of decision support tools, e-learning and computer-mediated communication. I am a member of The Danish Mathematical Society (DMF), The Danish Society for Theoretical Statistics (DSTS) and an individual member of the European Mathematical Society (EMS). Note: Comments published here are purely my own and do not reflect those of my current or future employers or other organizations.